首页|Data on Artificial Intelligence Reported by James M. Hillis and Colleagues (The potential clinical utility of an artificial intelligence model for identificatio n of vertebral compression fractures in chest radiographs)
Data on Artificial Intelligence Reported by James M. Hillis and Colleagues (The potential clinical utility of an artificial intelligence model for identificatio n of vertebral compression fractures in chest radiographs)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Artificial Intelligenc e is the subject of a report. According to news originating from Boston, Massach usetts, by NewsRx correspondents, research stated, "To assess the ability of the Annalise Enterprise CXR Triage Trauma artificial intelligence model to identify vertebral compression fractures on chest radiographs and its potential to addre ss undiagnosed osteoporosis and its treatment. This retrospective study used a c onsecutive cohort of 596 chest radiographs from four U.S. hospitals between 2015 and 2021." Our news journalists obtained a quote from the research, "Each radiograph includ ed both frontal (anteroposterior or posteroanterior) and lateral projections. Th ese radiographs were assessed for the presence of vertebral compression fracture in a consensus manner by up to three thoracic radiologists. The model then perf ormed inference on the cases. A chart review was also performed for the presence of osteoporosis-related ICD-10 diagnostic codes and medication use for the stud y period and an additional year of follow up. The model successfully completed i nference on 595 cases (99.8%); these cases included 272 positive ca ses and 323 negative cases. The model performed with area under the receiver ope rating characteristic curve of 0.955 (95% CI: 0.939 to 0.968), sen sitivity 89.3% (95% CI: 85.7 to 92.7%) and specificity 89.2% (95% CI: 85.4 to 92.3% ). Out of the 236 true-positive cases (i.e., correctly identified vertebral comp ression fractures by the model) with available chart information, only 86 (36.4% ) had a diagnosis of vertebral compression fracture and 140 (59.3%) had a diagnosis of either osteoporosis or osteopenia; only 78 (33.1% ) were receiving a disease modifying medication for osteoporosis. The model iden tified vertebral compression fracture accurately with a sensitivity 89.3% (95% CI: 85.7 to 92.7%) and specificity of 89.2% (95% CI: 85.4 to 92.3%)."
BostonMassachusettsUnited StatesNo rth and Central AmericaArtificial IntelligenceCompression FracturesEmergin g TechnologiesHealth and MedicineMachine LearningMetabolic Bone Diseases a nd ConditionsMusculoskeletal Diseases and ConditionsOsteoporosis
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